A state space approach to forecasting the final vintage of revised data with an application to the index of industrial production

There is considerable interest in the index of industrial production (IIP) as an indicator of the state of the UK's industrial base and, more generally, as a leading economic indicator. However, this index, in common with a number of key macroeconomic time series, is subject to revision as more information becomes available. This raises the problem of forecasting the final vintage of data on IIP. We construct a state space model to solve this problem which incorporates bias adjustments, a model of the measurement error process, and a dynamic model for the final vintage of IIP. Application of the Kalman filter produces an optimal forecast of the final vintage of data.     

Forecasting key macroeconomic variables from a large number of predictors: a state space approach

We use state space methods to estimate a large dynamic factor model for the Norwegian economy involving 93 variables for 1978Q2–2005Q4. The model is used to obtain forecasts for 22 key variables that can be derived from the original variables by aggregation. To investigate the potential gain in using such a large information set, we compare the forecasting properties of the dynamic factor model with those of univariate benchmark models. We find that there is an overall gain in using the dynamic factor model, but that the gain is notable only for a few of the key variables. Copyright © 2009 John Wiley & Sons, Ltd.     

A stochastic proportional hazard model for the force of mortality

In order to avoid ‘frailty’ in deterministic assumptions concerning survival law, in this paper stochastic volatility in the force of mortality is considered. In particular, mortality rates are studied by means of a stochastic model of CIR type. A method for estimating its parameters is presented and an example of application, based on simulations of the process, is shown. Empirical results and comparison with a traditional model illustrate predictive performance and the flexibility of the model. Copyright © 2006 John Wiley & Sons, Ltd.     

Forecasting real‐time data allowing for data revisions

A modeling approach to real‐time forecasting that allows for data revisions is shown. In this approach, an observed time series is decomposed into stochastic trend, data revision, and observation noise in real time. It is assumed that the stochastic trend is defined such that its first difference is specified as an AR model, and that the data revision, obtained only for the latest part of the time series, is also specified as an AR model. The proposed method is applicable to the data set with one vintage. Empirical applications to real‐time forecasting of quarterly time series of US real GDP and its eight components are shown to illustrate the usefulness of the proposed approach. Copyright © 2007 John Wiley & Sons, Ltd.     

Human judgments in New York state sales and use tax forecasting

Human judgments have become quite important in revenue forecasting processes. This paper centres on human judgments in New York state sales and use tax by examining the actual practices of information integration. Based on the social judgment theory (i.e., the lens model), a judgment analysis exercise was designed and administered to a person from each agency (the Division of the Budget, Assembly Ways and Means Committee Majority and Minority, and the Senate Finance Committee) to understand how information integration is processed among different agencies. The results of the judgment analysis exercise indicated that revenue forecasters put different weight on cues. And, in terms of relative and subjective weights, the cues were used differently, although they were presented with the same information. Copyright © 2004 John Wiley & Sons, Ltd.     

A new Markov regime-switching count time series approach for forecasting initial public offering volumes and detecting issue cycles

This study proposes a novel Markov regime-switching negative binomial generalized autoregressive conditional heteroskedasticity model for analyzing count data time series. We develop a likelihood-based method for parameter estimation and give the one-step-ahead forecasting algorithms for the mean, variance, and quantiles. An empirical analysis of both the U.S. initial public offering (IPO) and Chinese A-share IPO markets indicates that our method is very efficient in forecasting monthly IPO volumes and detecting hot/cold issue markets. The first-day IPO return is positively correlated with the IPO volume in a hot issue market but negatively correlated with the IPO volume in a cold issue market, in both the U.S. and Chinese IPO markets. However, the average first-day return in the previous hot issue market has a significant positive impact on the current IPO volume for only the U.S. IPO market. Our approach helps to more accurately model and understand the behavior of hot/cold IPO issue markets.     

亚90nm沟道MOSFET亚阈值状态下二维电势和阈值电压的半解析模型

本文提出一个亚90nm沟道MOSFET在亚阈值状态下的二维电势和阈值电压的半解析模型．文章首先根据短沟道MOSFET在亚闽值状态下的物理模型提出定解问题,然后用特征函数将由氧化层和空间电荷区衔接条件所得到的超越方程组作正交展开,得到关于未知量的线性代数方程组．求出了氧化层和空间电荷区的二维电势、耗尽层厚度和阈值电压的表达式．该模型不需要适配参数,运算量小,避免了方程离散化,计算精度与数值解精度相同．文章给出了沟道长度为90nm以下MOSFET的电势分布、表面势、耗尽层厚度和阈值电压计算结果．计算值与二维数值模拟值高度吻合．